You might have heard the term Data lake architecture before, but it is understandable if you are not really sure what it means. With so many different ways of handling data, it can be tough to remember exactly what they all are or how they are meant to work together for businesses with multiple locations.
Data lake architecture uses the concept of a data lake to store a vast amount of data in an unstructured and freeform way. More and more companies have been considering how to integrate a data lake into their own systems – but why, and what does it change?
Understanding Data Lakes
A data lake is a centralized storage repository that stores all structured, unstructured, and semi-structured data in its original format. The idea behind it is to create a full “lake” of information – everything is available and easy to access, all placed in the same basic system rather than being split between storage methods or servers.
In simple terms, a data lake is a central location that houses all of your raw data. Unlike a conventional “data warehouse” that uses a hierarchal structure of files and folders, a data lake puts everything in the same place. This is quick, simple, inexpensive, and means that all raw data is readily available as needed.
The point of a data lake is to get around the limitations of using a structured warehouse-like system. Instead of different points of data being contained in their own boxes, all information is spread out in the same pool of data, with folders and subfolders being used only when necessary.
Why is Data Lake Architecture Good?
Data lakes were developed to get around the heavy restrictions of relying on data warehouse systems for everything. While a data warehouse can provide a lot of structure and make it easier to segment data into its own space, a data lake is all about combining and mixing that data to find new possibilities or advantages that you would otherwise miss.
Data warehouses are also often more expensive to maintain, especially if your business is using specialized tools to structure everything. A data lake is just a flat plain where you dump everything, making it cheaper and far more convenient for quick and short-notice use. Since data lakes are meant for raw data, they can become an incredibly practical choice in data-heavy industries.
One of the core advantages of a data lake is that it can process any data. No matter how refined the information is, it can be stored in exactly the same space as fully-processed data. While this might sound like it would create a big mess, there are some core advantages to doing this.
The open-format nature of a data lake means that users are not going to be locked into proprietary systems or have to deal with file-type limitations. They can be managed incredibly easily and do not have many restrictions or limitations of their own, making them highly versatile.
Since they can accept any kind of data, a decent data lake can become a great “dumping ground” for any and all information. This information can then be used as needed or stored until it is processed into a more refined form that can be used for a practical purpose.
Centralizing a large part of your data removes the issue of having separate data silos that are not in constant communication with one another. Too many data silos, especially ones hosted in different ways or through different tools, can lead to data duplication and issues with collaboration.
Aiming for a centralized data lake can also allow you to offer better security features, keeping your data safer by focusing all of your efforts on one storage method. These can be significant changes if your business handles a lot of sensitive data that needs to be processed quickly and consistently.
Data lakes are also perfect for combining with machine learning, data science, or SQL analytics. Raw data can be retained at an incredibly low cost and fed to all kinds of machine learning or analysis programs – and since all the data is in one place, you just need to point the tool at the data lake itself.
Since there are so many different ways to process your raw data, you have near-infinite possibilities with how you use your data lake for this kind of work. This is best done using data lakehouse architecture, a combination between warehouse architecture and data lakes that can allow you to process information incredibly fast.
Using lake house architecture gives you an easy way to integrate information with other tools or to draw data from a huge range of sources. You can collect all kinds of data in your data lake architecture, meaning that you can gather it from nearly any other tool without issue.
This means that you can store anything from videos and images to binary code and unformatted documents in your system, processing them as needed without having to set up a specific storage system for each and every one. This added simplicity is a huge benefit.
Democratized Self-Service Storage
The inherent flexibility of data lake architecture, even compared to options like Azure data warehouse architecture, means that users can approach the lake in a range of different ways. No matter your skills and tools, you can gather the data you need within minutes and then begin to process it yourself.
This democratic approach to data means that no raw data is locked behind access to other servers unless it gets intentionally placed somewhere else. If somebody needs to gather information on how a particular tool or service performed, then the raw data is right there, ready to copy and process at their leisure.
This also removes the issue of duplicate data causing confusion. There is only one place to get raw data, so there will not be dozens of copies of the same file clogging up multiple folders or getting lost in people’s personal files for hours. Data is stored safely and accessibly, making it ready to use at a moment’s notice.